e.g. mhealth
Search Results (1 to 4 of 4 Results)
Download search results: CSV END BibTex RIS
Skip search results from other journals and go to results- 1 Interactive Journal of Medical Research
- 1 JMIR Aging
- 1 JMIR mHealth and uHealth
- 1 Journal of Medical Internet Research
- 0 Medicine 2.0
- 0 iProceedings
- 0 JMIR Research Protocols
- 0 JMIR Human Factors
- 0 JMIR Medical Informatics
- 0 JMIR Public Health and Surveillance
- 0 JMIR Serious Games
- 0 JMIR Mental Health
- 0 JMIR Rehabilitation and Assistive Technologies
- 0 JMIR Preprints
- 0 JMIR Bioinformatics and Biotechnology
- 0 JMIR Medical Education
- 0 JMIR Cancer
- 0 JMIR Challenges
- 0 JMIR Diabetes
- 0 JMIR Biomedical Engineering
- 0 JMIR Data
- 0 JMIR Cardio
- 0 JMIR Formative Research
- 0 Journal of Participatory Medicine
- 0 JMIR Dermatology
- 0 JMIR Pediatrics and Parenting
- 0 JMIR Perioperative Medicine
- 0 JMIR Nursing
- 0 JMIRx Med
- 0 JMIRx Bio
- 0 JMIR Infodemiology
- 0 Transfer Hub (manuscript eXchange)
- 0 JMIR AI
- 0 JMIR Neurotechnology
- 0 Asian/Pacific Island Nursing Journal
- 0 Online Journal of Public Health Informatics
- 0 JMIR XR and Spatial Computing (JMXR)
Go back to the top of the page Skip and go to footer section

Wicks gained considerable public attention during the COVID-19 pandemic through his web-based workout sessions designed to promote physical exercise during lockdowns.
During the first quarter of 2020, most countries globally were affected by the onset of the COVID-19 pandemic [8]. A feature of lockdowns across the world was the compulsory closure of schools.
J Med Internet Res 2024;26:e49921
Download Citation: END BibTex RIS

Four apps suggested prefabricated workout plans that were customizable in 3 of these apps. One app provided motion detection of the exercising person using the camera of the smartphone. The characteristic elements and different combinations used for each app are listed in Table 1.
JMIR Mhealth Uhealth 2023;11:e47502
Download Citation: END BibTex RIS

This study aimed to use deep neural networks to design and develop a personal workout assistant capable of providing feedback on squat postures using only mobile devices such as smartphones. In the first part of this study, a squat video data set was created and a deep learning model using a combination of pose estimation and video classification was trained to analyze workout postures.
Interact J Med Res 2023;12:e37604
Download Citation: END BibTex RIS